class_docstr: Group together categories which occurence is less than a specified
  minimum fraction. Coalesced categories get index of one.
code: def fit(self, X, y=None):
      self.check_X(X)
  
      if self.minimum_fraction is None:
          return self
  
      # Remember which values should not be coalesced
      do_not_coalesce = list()
      for column in range(X.shape[1]):
          do_not_coalesce.append(set())
  
          if sparse.issparse(X):
              indptr_start = X.indptr[column]
              indptr_end = X.indptr[column + 1]
              unique, counts = np.unique(
                  X.data[indptr_start:indptr_end], return_counts=True
              )
              colsize = indptr_end - indptr_start
          else:
              unique, counts = np.unique(
                  X[:, column], return_counts=True
              )
              colsize = X.shape[0]
  
          for unique_value, count in zip(unique, counts):
              fraction = float(count) / colsize
              if fraction >= self.minimum_fraction:
                  do_not_coalesce[-1].add(unique_value)
  
      self.do_not_coalesce_ = do_not_coalesce
      return self
  
file_path: auto-sklearn/autosklearn/pipeline/implementations/MinorityCoalescer.py
imports: import numpy
  from scipy import sparse
  from sklearn.base import BaseEstimator, TransformerMixin
label_desc: Use image node_id 3 for calling the MinorityCoalescer obj's underlying member method code with example usage: obj.fit(X, y) and returns: self, self